Supporting data for the thesis "Development of GaN microchip for label-free and scalable monitoring of single-cell dynamic behaviors"

Accurately elucidating the intricate and dynamic behaviors of cells, along with their underlying mechanisms, is crucial for advancing the understanding of fundamental biological principles, improving disease diagnostics, and accelerating drug development. Conventional population-based approaches, which emphasize general patterns across cell populations, often overlook rare events and individual cell heterogeneity. In contrast, single-cell analysis techniques can effectively address this limitation by enabling discrete observations of individual cells.

However, the most popular fluorescence-based single-cell techniques face significant challenges like photobleaching, chemical invasiveness, and potential biological perturbations, making them less suitable for long-term continuous sensing. Although label-free surface plasmon resonance-based methods can overcome these constraints, they are still hindered by high dependence on bulky and costly external optical/electrical equipment for scalable measurement and large-scale deployment.

To overcome these barriers, this thesis aims to develop a highly integrated, scalable biosensor capable of label-free, real-time, long-term single-cell analysis. Furthermore, through various signal transduction pathways, individual cells inherently can perceive, detect, and respond to extracellular changes, making it possible to function as a biosensor for high-sensitivity detection of the external environment (e.g., extracellular matrix, neighboring cells). Hence, this paper also focuses on how individual cells dynamically sense and respond to their surroundings, providing insights into developing cell-based single-cell sensors.